UBI Probably Won’t Happen

The futurist vision behind AI and UBI assumes that once material scarcity is reduced, human flourishing will naturally follow.

I am not convinced that is true.

Human beings do not merely need resources. They need meaning, responsibility, contribution, structure, and purpose. Throughout history, work has provided far more than income. It has provided identity, discipline, social connection, competence, and a sense of usefulness.

Even if AI eventually creates enough abundance to support some form of universal income, that does not answer the deeper question of what people are supposed to do with themselves afterward.

And even before we reach that philosophical problem, there is the practical reality that large-scale systems require enormous coordination between governments, corporations, financial institutions, and voters who rarely agree on anything for very long.

In theory, UBI sounds simple:
“Just distribute the wealth generated by automation.”

In practice, that requires herding more cats than humanity has ever successfully herded before.

The Source

I recently watched this Vishen Lakhiani video:

He attended one of Peter Diamandis’ events and got permission to share the vision presented.

The vision as simplistic as I can make it is that AI and robotics will automate most human functions and drive the cost of production very low. The human work week will will be reduced to one or two days. Through consolidating government programs like Medicare and Social Security, they’ll be able to provide a UBI (Universal Basic Income) to everybody of $3000 a month, but the cost of everything will be so low we’ll all live prosperously on that amount.

The Expanse.

I’ve read all nine Expanse novels and seen all six seasons of the show that SyFy killed after three seasons (because they’d rather make room for ghosts and wrestling) but Amazon rescued the show and produced three more seasons. In The Expanse universe, most of humanity is on a welfare known as “Basic”. Some people win a lottery and are able to leave the planet and work jobs in space, but this isn’t available to most. That’s what this makes me think of, and I do not desire that kind of existence.

The Reality

I think some of the predictions are accurate, specifically about the disruption AI will cause, and maybe even some of the prosperity and leisure it will provide. Some jobs will be automated away and some will change. Others will remain unaffected (trades). I have no dispute with the technical part of things.

AI will also likely create new jobs that nobody sees coming. For instance, somebody will have to set the policies AI follows along with guardrails. Much like the car eliminated buggy whip manufacturers but created jobs for mechanics, AI will follow a similar path.

One prediction I am genuinely excited about is AI doctors.

Imagine never having to sit in a waiting room again because no doctor on Earth can consistently honor appointment times. Then the doctor finally comes in, already thinking about the next twenty patients while trying to document everything in real time. If you forgot an important question or need time to process the answer, you are often stuck waiting until the next appointment.

An AI doctor changes that dynamic completely.

You could access it at any time. You could walk away from the conversation, think about the information, research something, and come back later to continue. You could even challenge it:
“But Dave Asprey says…”

That part actually excites me.

Not because I think AI should fully replace human doctors, but because it could finally give patients something modern healthcare rarely provides: time, attention, and continuity of thought.

As for the work week, I don’t think we’ll see much reduction anytime soon, and UBI will probably remain a dream of tech utopians a lot longer than they predict.

Too Many Cats To Herd

One reason I remain skeptical of UBI timelines is that they assume institutions adapt rationally and efficiently.

My own experience says otherwise.

I’ve believed for years that the modern work model is largely obsolete for knowledge work. We still expect people to wake up, drive to a building, sit in assigned spaces for eight or nine hours, and then drive home, even when much of the work could be done more flexibly and often more effectively elsewhere.

Yet despite years of studies, experiments, and real-world examples, many organizations continue pushing for centralized in-person work because institutions resist change long after the original conditions that created them disappear.

The same applies to work hours themselves. Much of our modern schedule is still inherited from industrial-era assumptions built around factories and physical labor. Knowledge work does not function the same way. Ideas do not arrive on command between 8 AM and 5 PM.

If organizations struggle this much to modernize remote work policies, I have a hard time believing they will smoothly coordinate a complete restructuring of labor, entitlement systems, taxation, and wealth distribution across society.

That is an entirely different level of complexity.

The Cats That Will Never Be Herded

As soon as I heard the part about combining Social Security and Medicare, I knew this is not going to happen. Large bureaucratic systems tend to accumulate overlapping programs, administrative layers, and competing incentives over time. Streamlining them sounds simple in theory, but in practice every program develops constituencies, dependencies, political protections, and institutional inertia.

That makes large-scale simplification extraordinarily difficult.

This plan also assumes robotics and automation will drive prices to the floor. But we’re seeing how much money it takes to build and run a data center. Communities are fighting against data centers because of the noise and increases in water and electric costs. Nobody wants that. There is a plan to put a data center across the street from my neighborhood. Screw that. Build it in a desert somewhere. I already have to run brown noise at night to cover highway and construction noise that keeps me awake. I don’t need infrasound and that constant low frequency hum.

We’re also seeing a lot of businesses scale back from using AI because of token cost or the realization that some things require a human in the loop.

Once the AI bubble pops, funding for data centers will dry up. I don’t think this timeline is going to be as fast and sure as they assume.

Then you have to trust the Federal Reserve not to inflate the currency away. Even if we hit a point where $3000 a month leads to a prosperous existence, that will change within a few years as the dollar loses more value due to constant printing. I sure don’t trust a government, or even a billionaire tech bro, to decide how much I should have to live on.

Money Isn’t the Meaning of Life

We all need money to survive. Modern life is expensive, and every adult understands the endless list of obligations required simply to exist:

  • housing
  • transportation
  • insurance
  • utilities
  • food
  • healthcare
  • taxes

But human beings do not live on consumption alone.

Work provides more than income. It provides structure, responsibility, competence, discipline, social connection, and a sense that we are contributing something meaningful beyond ourselves.

That is one reason retirement is often psychologically difficult, especially for men. Many people imagine endless leisure will naturally produce happiness, only to discover that entertainment is not the same thing as purpose.

The futurist vision of AI and UBI often assumes that once material needs are met, human flourishing automatically follows.

I suspect reality is more complicated.

A civilization can subsidize income. It is much harder to subsidize meaning.

And if AI eventually reduces the economic need for large amounts of human labor, the real crisis may not be financial at all. It may be existential.

What happens when millions of people no longer believe they are needed?

Will AI Take Our Jobs?

Probably not. At least not all at once.

It will absolutely change many jobs. Some jobs may disappear entirely. Others will be heavily augmented. But I suspect the real future will be messier and slower than either the evangelists or the doomers predict.

Disruptions Do Happen

Technological disruption is nothing new.

The light bulb turned the candle industry into a niche. Cars replaced horse-drawn transportation and wiped out entire supporting industries. “Video killed the radio star,” at least until MTV stopped playing music videos.

Technology changes markets. It changes labor. It changes how people organize society. AI is not unique in that regard.

What is unique is the speed of development and the amount of capital currently flowing into it.

The Projection Problem

Human beings are very good at taking present trends and projecting them indefinitely into the future.

When the stock market is booming, people assume it will continue forever. When housing prices rise, people pile in. People warning about bubbles are mocked right up until the correction happens.

The same thing happens with technology.

As of early 2026, NVIDIA became the highest valued company on the planet. History suggests that eventually growth plateaus, competitors emerge, margins compress, or the market adjusts expectations. That does not mean AI is fake. It means hype cycles are real.

AI itself is much older than most people realize. The field dates back to the 1950’s, and there have already been multiple “AI winters” where funding dried up after expectations outran practical results.

A few years ago, I assumed we would eventually hit the limits of current hardware and enter another AI winter. So far that prediction has not happened. Instead, capability keeps accelerating while companies continue releasing increasingly powerful models and tools.

But history also suggests exponential curves rarely continue forever.

What AI Is Actually Good At

Most of the public discussion around AI seems centered on programming. That’s not my world. I’m not a developer, and I have no desire to become one.

What interests me more are practical cognitive applications.

I use AI almost every day:

  • evaluating journal entries and spotting patterns in my own thinking
  • summarizing long-form content that may not justify reading the entire source
  • brainstorming and research
  • organizing information faster than I could manually

One of the most useful applications I’ve found at work is summarizing email chains.

Anyone who has worked in large organizations knows the pain of digging through endless headers, signature blocks, reply chains, and side conversations just to locate the one relevant sentence. AI is remarkably good at extracting the actual signal from the noise.

I can paste a chain into AI and have it summarize:

  • the discussion
  • the stakeholders involved
  • decisions required
  • unresolved issues
  • action items

It will even generate clean decision tables I can hand directly to leadership.

That is real value.

Fragmentation

At the same time, AI still feels strangely disconnected.

I have access to Microsoft Copilot, but I use Apple Calendar and do not use Outlook.com as my primary email service. I configured MCP integration between ChatGPT and Notion, but sometimes it works and sometimes it cannot see my information at all.

The technology is advancing rapidly, but the ecosystem still feels fragmented and incomplete.

What many people actually want is not just a chatbot. They want an integrated cognitive assistant that understands context across the systems they already use and can proactively anticipate needs.

We are not fully there yet.

What AI Still Cannot Do

As a Project Manager, I can already see areas where AI performs parts of my job faster than I can. Planning, summarization, documentation, and even aspects of risk analysis can often be automated or heavily accelerated.

But there are still critical parts of organizational life AI does not handle well.

Two of the biggest are:

  • cat herding
  • horse trading

Large organizations are not pure logic systems. They are human systems.

Projects succeed or fail based on personalities, incentives, politics, negotiation, trust, competing priorities, and institutional realities. Much of leadership is not about generating the optimal answer. It is about aligning human beings who want different things.

AI can assist with decision support. It can identify patterns and blind spots. But it has no agency and cannot be held accountable.

That distinction matters.

An AI can recommend a project plan. It cannot own the consequences if the plan fails.

A human still has to bear responsibility.

Jobs at Risk

Most jobs will probably be augmented before they are fully replaced.

If your work consists primarily of processing information according to repeatable patterns, your role is likely vulnerable to some level of automation. Some service industries and white-collar professions may experience major disruption:

  • travel agents
  • portions of law
  • accounting
  • consulting
  • financial planning
  • administrative HR functions

I have already seen prompts that can produce work products which would have previously required expensive consulting engagements.

That should concern people in those industries.

The trades, however, appear relatively safe for now. Mechanics, HVAC technicians, plumbers, electricians, builders, and similar roles operate in the physical world under constantly changing real-world conditions. Robotics may eventually change some of that, but physical labor remains far harder to automate than many knowledge workers realize.

Conclusion

The mistake many people make is assuming that because AI can simulate parts of intelligence, it can replace the entirety of human work.

But organizations are not held together by information processing alone. They are held together by trust, negotiation, responsibility, incentives, politics, and human relationships.

AI is already extremely useful. I use it constantly. It saves time, identifies patterns, summarizes information, and improves my ability to think through problems. In many areas, it will absolutely reduce the number of people required to perform certain kinds of work.

But there is a difference between generating outputs and carrying responsibility.

AI can recommend. It cannot own consequences.

That distinction may shape the next decade more than the technology itself.

The future is probably not full automation replacing humanity overnight. It is more likely a long period of uneven disruption where people who effectively collaborate with AI outperform those who ignore it, while institutions struggle to adapt to the economic and social consequences.

And if large portions of cognitive labor really do become heavily automated, society will eventually be forced to answer a deeper question:

What happens when human economic value is no longer tied as directly to human labor?

That may become the real debate behind AI.

Verified by ExactMetrics